DOE OSTI2013
The focus of this initial work is to apply recent breakthroughs in sparse linear modeling and nonlinear signal processing to the problem of removing microphonic noise from nuclear spectrum measurements. The resolution of nuclear spectrum measurements is commonly degraded by microphonic noise caused by the vibration of supporting equipment (i.e. cryocoolers) as well as motion of the detector itself in mobile applications. Current signal processing techniques cannot remove microphonic noise without modifying the desired characteristics of the spikes induced by nuclear detection events. The LANL Engineering Institute has been working with NEN‐1 to remedy this problem. Initial work focused on developing active control systems to cancel the vibrations experienced by nuclear detectors. As work on progressed along these lines the team realized that a more robust, lower‐energy, lower‐cost solution to the microphonic noise problem could be realized using recent breakthroughs in nonlinear signal processing. Over the course of the last decade significant advances have been made in the field of sparse linear modeling using L1 norm minimization to find linear representations of sparse signals. The problem of removing microphonic noise from nuclear detection measurements appears to be well suited to the L1 minimization approach because these measurements tend to be inherently sparse in two different domains. The nuclear detections are sparse in a spike domain, and the microphonic noise is sparse in the Fourier basis. L1 minimization allows us to decompose the measurements into these two domains. The result is that we can potentially separate the spikes from the microphonic noise. Initial investigations into this technique look very promising. In this work microphonic noise is induced in an HE‐3 detector by a wrench impact. A pulser is used to generate uniform, synthetic, nuclear events and is fed into the detector front end electronics. Nearly all the microphonic noise is separated from the spike measurements. The histograms in Figure 2 illustrate that spectrum widening caused by noise is significantly reduced. The goal of future work to further develop this technique to account for variations in spike characteristics, as well as to use adaptive signal processing and modern Graphical Processing Unit (GPU) technology to speed up the signal decompositions.